Introduction to Machine Learning using Python: Classification

In this live coding workshop, we provide a comprehensive introduction to the Classification models in Machine Learning and use Python to apply the knowledge on real-world datasets. We hope after this hands-on workshop, you will have a better understanding of these Machine Learning models and techniques and appreciate its capability, as well as make better informed decisions on how to leverage Machine Learning in your research.

Please read carefully the prerequisites and make sure you have the required knowledge.

For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:

• Introduction to Machine Learning using Python: Introduction & Linear Regression
• Introduction to Machine Learning using Python: Classification
• Introduction to Machine Learning using Python: SVM & Unsupervised Learning

Prerequisites:

• Have attended the “Introduction to Machine Learning using Python: Introduction & Linear Regression” course
• Good understanding of Python syntax and basic programming concepts
• Familiar with Pandas, Numpy and Seaborn libraries
• Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.

Learning Outcomes:

• Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
• Know the differences between various core Machine Learning models.
• Understand the Machine Learning modelling workflows.
• Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.

Why do this course?

• Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
• It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
• We do have applications on real datasets!
• Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
• Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.

For more information about this course please visit our website: https://intersect.org.au/training/course/python206/(opens in a new window)